Automated systems exist that enable data to be visualized on top of a map so that such data is easy to view. For example, such a system may enable a large amount of environmental data that is retrieved from environmental data collection stations to be visualized on top of a geographical map. Such environmental data may include, for example, atmospheric concentrations of particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), or the like. In further accordance with such an example, sampling data that is periodically obtained from the environmental data collection stations may be used to generate and periodically update a colored map of a specific area (e.g., a city, state or country) that illustrates the degree of pollution.
A conventional method for generating the colored map involves performing interpolation, classification and coloring of each display unit (e.g., each pixel) of the colored map image one by one. In accordance with this conventional method, the first step is to calculate an interpolated data value for each display unit in the colored map image that is not already associated with a sampled data value based on the available sampled data values. After this, each display unit is assigned to a particular class based on its associated sampled or interpolated data value. Then, each display unit is colored based on its assigned class in accordance with a reference coloring table. This method is problematic in that it is time consuming, as an interpolated data value must be calculated for each display unit in the colored map image that is not already associated with a sampled data value. This method is also computationally complex. For example, for a colored map image that is square in shape and has N display units per side, the complexity of this method is on the order of N2.